The present disclosure relates to a system and method for performing camera diagnostics. The system operates using a multiple phase approach to detect and confirm camera deterioration. The present disclosure finds application in traffic surveillance. However, it is appreciated that the present exemplary embodiments are also amendable to other like applications.
Conventional traffic cameras are used to monitor traffic flow and survey vehicle compliance with traffic laws, rules, and regulations. A conventional traffic camera can support a variety of surveillance tasks. Generally, this type of camera selectively captures video of a scene that is being surveyed. These cameras may then transmit image data to a remote server device for further processing, or may perform some or all of the processing on-board. Because the image data is often used for fee collection and traffic enforcement, the ability of the camera to accurately render an image is essential to the performance of a traffic monitoring system.
However, performance of a camera deteriorates over time. The deterioration can be gradual or sudden and can affect the camera's ability to perform its desired function. One example of decreased performance results when the camera captures blurred images caused by a dirty or smudged lens. Deterioration can also result from camera misorientation, which may be caused, for example, by impact to the camera housing. Other examples of deterioration include low image contrast resulting from flash defects and failures and near field camera obstruction resulting from a variety of causes.
While total failure can easily be detected by automatic means, subtle deterioration is more difficult to detect and is further complicated by the many possible sources of deterioration including, for example, weather, vehicle speeds, and vehicle conditions. As an example, displacement of a traffic camera's field of view (FOV) can occur when the camera position and/or orientation is moved by a force of wind, accumulated snow/ice, and/or inadequately secured camera mounts, etc. Because traffic cameras are positioned in or near traffic flow, routine on-site camera inspections are difficult. On-site inspections can disrupt traffic and/or place technicians at risk of oncoming traffic.
Conventional approaches for detecting subtle deterioration in traffic camera diagnostics include test pattern or scene analysis. The test pattern approach uses images with specially designed and well characterized objects that are placed in the field of view of the camera to detect and/or quantify output quality by applying a process similar to one used to evaluate the quality of images rendered by multifunction printer devices. The test patterns are objects with known patterns (e.g. bright reflectors on a 1 ft×1 ft grid) or with readily identified features (e.g. dark painted lines of known dimensions on a plane surface) placed on, for example, a vehicle that travels across the scene that is being surveyed by the traffic camera. However, the labor and resources required for implementing the traffic test pattern approach are significant. Because test pattern analysis is expensive, it should not be invoked unless clearly needed.
The alternative approach is scene analysis, which performs image analysis on all or part of the scene that is captured by the camera. A common shortcoming of scene-dependent image analysis is its tendency to yield lower quality and scene-dependent diagnostics signals that are sub-optimal (or even intractable) for diagnostics. This drawback can be worse if this approach is used for measuring small amounts of camera misalignment (e.g. gradual deterioration over time). Furthermore, because scene elements and noise effects can confound diagnostic signals that are extracted from the image, the performance of scene analysis for camera diagnostics varies depending on the scene.
Gaps remain and limit the capability of traffic camera diagnostics when these conventional approaches are used independent of one another. A method and system for performing traffic camera diagnostics are therefore desired which maximize the strengths of both approaches by using scene analysis for predicting or identifying a potential misalignment and then using test pattern analysis to make a final decision and/or correction.